Associate-Data-Practitioner Exam Dumps - Google Cloud Platform Questions and Answers

To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.

Assigning each analyst to a separate project associated with their department and creating asingle reservation for each departmentusingBigQuery editionsallows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.

Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.

Extract from Google Documentation: From "Row-Level Security in BigQuery" (https://cloud.google.com/bigquery/docs/row-level-security ):"Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user’s region, providing fine-grained control over data visibility without creating separate tables or views."

Using Cloud Data Fusion to create a batch pipeline with a Cloud Storage source and a BigQuery sink is the best solution because:

Scalability: Cloud Data Fusion is a scalable, fully managed data integration service.

Data transformation: It provides a visual interface to design pipelines, enabling quick transformation of data.

Data quality insights: Cloud Data Fusion includes built-in tools for monitoring and addressing data quality issues during the pipeline creation and execution process.

Creating amaterialized view in BigQuerywith theSUM()function and theDATE_SUB()function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using theDATE_SUB()function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.

UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.

Comprehensive and Detailed In-Depth Explanation:

For a 25 TB dataset, efficiency and cost require minimizing data movement and leveraging BigQuery’s scalability within Colab Enterprise.

Option A: Exporting 25 TB to Google Drive and loading via Pandas is impractical (size limits, transfer costs) and slow.

Option B: BigQuery magic commands (%%bigquery) in Colab Enterprise allow direct querying of BigQuery data, keeping processing in the cloud, reducing costs, and enabling collaboration.

Option C: Dataproc with Spark adds cluster costs and complexity, unnecessary when BigQuery can handle the workload.

Creating acustom field from the field picker in Lookerallows you to add new fields to your report without requiring theDevelop permission. Custom fields are created directly in the Looker UI, enabling you to define calculations or transformations that generate SQL for the database query. This approach is user-friendly and does not require access to the LookML layer, making it the appropriate choice for your situation.

Copying the storm event dataset into yourBigQuery projectand usingBigQuery Studioto query and visualize the data inLooker Studiois the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.

Comprehensive and Detailed In-Depth Explanation:

Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model. BigQuery ML supports this use case with logistic regression.

Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.

Option B: Matrix factorization is for recommendation systems, not binary prediction.

Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.